scholarly journals Optimum Experimental Design for Nonlinear Process Models

PAMM ◽  
2011 ◽  
Vol 11 (1) ◽  
pp. 719-720
Author(s):  
Stefan Körkel
Keyword(s):  
Experimental Design ◽  
Nonlinear Process ◽  
Process Models ◽  
Optimum Experimental Design

Identifying suboptimalities with factorial model comparison

2018 ◽  
Vol 41 ◽  
Author(s):  
Wei Ji Ma
Keyword(s):  
Experimental Design ◽  
Model Comparison ◽  
Process Models ◽  
Multiple Forms ◽  
Factorial Experimental Design ◽  
Factorial Model ◽  
Multiple Components ◽  
The Many

AbstractGiven the many types of suboptimality in perception, I ask how one should test for multiple forms of suboptimality at the same time – or, more generally, how one should compare process models that can differ in any or all of the multiple components. In analogy to factorial experimental design, I advocate for factorial model comparison.

scholarly journals Towards Enhanced Performance of Neural-Network-Based Fault Detection Using an Sequential D-Optimum Experimental Design

2018 ◽  
Vol 8 (8) ◽  
pp. 1290 ◽  
Author(s):  
Beata Mrugalska
Keyword(s):  
Neural Network ◽  
Experimental Design ◽  
Fault Detection ◽  
Network Models ◽  
Neural Model ◽  
Neural Network Models ◽  
Optimum Experimental Design ◽  
The Neural Network ◽  
Adaptive Thresholds ◽  
Linear Neural Network

Increasing expectations of industrial system reliability require development of more effective and robust fault diagnosis methods. The paper presents a framework for quality improvement on the neural model applied for fault detection purposes. In particular, the proposed approach starts with an adaptation of the modified quasi-outer-bounding algorithm towards non-linear neural network models. Subsequently, its convergence is proven using quadratic boundedness paradigm. The obtained algorithm is then equipped with the sequential D-optimum experimental design mechanism allowing gradual reduction of the neural model uncertainty. Finally, an emerging robust fault detection framework on the basis of the neural network uncertainty description as the adaptive thresholds is proposed.

scholarly journals Remediation of Heavy Metal (Cd, Cr, Cu, Co, and Ni) Ions from Kaolinite Clay Using Molecular Micelles Chelators and D-Optimum Experimental Design

2013 ◽  
Vol 04 (08) ◽  
pp. 789-795 ◽  
Author(s):  
Sayo O. Fakayode ◽  
Ashley M. Taylor ◽  
Maya McCoy ◽  
Sri Lanka Owen ◽  
Whitney E. Stapleton ◽  
...  
Keyword(s):  
Heavy Metal ◽  
Experimental Design ◽  
Kaolinite Clay ◽  
Optimum Experimental Design ◽  
Heavy Metal Cd

scholarly journals Robust Model Selection: Flatness-Based Optimal Experimental Design for a Biocatalytic Reaction

Processes ◽  
2020 ◽  
Vol 8 (2) ◽  
pp. 190
Author(s):  
Moritz Schulze ◽  
René Schenkendorf
Keyword(s):  
Experimental Design ◽  
Model Selection ◽  
Process Models ◽  
Differential Flatness ◽  
Optimal Controls ◽  
Parameter Uncertainties ◽  
Point Estimate Method ◽  
Biocatalytic Reaction ◽  
Improved Model ◽  
The Impact

Considering the competitive and strongly regulated pharmaceutical industry, mathematical modeling and process systems engineering might be useful tools for implementing quality by design (QbD) and quality by control (QbC) strategies for low-cost but high-quality drugs. However, a crucial task in modeling (bio)pharmaceutical manufacturing processes is the reliable identification of model candidates from a set of various model hypotheses. To identify the best experimental design suitable for a reliable model selection and system identification is challenging for nonlinear (bio)pharmaceutical process models in general. This paper is the first to exploit differential flatness for model selection problems under uncertainty, and thus translates the model selection problem to advanced concepts of systems theory and controllability aspects, respectively. Here, the optimal controls for improved model selection trajectories are expressed analytically with low computational costs. We further demonstrate the impact of parameter uncertainties on the differential flatness-based method and provide an effective robustification strategy with the point estimate method for uncertainty quantification. In a simulation study, we consider a biocatalytic reaction step simulating the carboligation of aldehydes, where we successfully derive optimal controls for improved model selection trajectories under uncertainty.

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